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API

Every metric the dashboard shows, available programmatically, so your AI visibility data can live wherever your team already works.
Steve Lee, Founder of SEO Aesthetic·Written July 14, 2026·Updated July 30, 2026·5 min read
On This Page
  1. 1. What the API exposes
  2. 2. Common uses
  3. 3. Getting started
Summary & Key Takeaways
  • Exposes visibility, citation share, traffic, and crawler data through a clean API.
  • Lets you pipe AEO metrics into your own BI, warehouse, or reporting.
  • Supports scheduled pulls and event webhooks.
  • Built so AEO data is a feed, not a walled garden.

1. What the API exposes

The API returns everything the platform measures: AI visibility scores, citation share by prompt and by competitor, AI-referred traffic, generative impressions, and answer-engine crawler activity. Because it mirrors the dashboard exactly, nothing important is trapped behind the interface, which keeps your Answer Engine Optimization data portable and yours. That portability is a deliberate stance, not an afterthought: a tool that only lets you see your data inside its own walls is renting it back to you. Here the numbers are a feed you own, available wherever your team already does its reporting.

2. Common uses

Teams use it to pipe AEO metrics into a warehouse or BI tool, to blend AI visibility with their own revenue and pipeline data, and to trigger alerts when a key prompt loses or gains a citation. The point is to make AI visibility a first-class feed alongside the rest of your reporting, not a separate tab someone remembers to check once a month. When a competitor displaces you on a prompt that drives real revenue, you want that in the same alert stream as a traffic drop or a failed deploy, because that is how it gets treated as the business event it actually is rather than a vanity metric.

3. Getting started

Access is token-based, with scheduled pulls and event webhooks both available, so you can poll on your own cadence or have the platform push changes to you the moment they happen. Most teams have data flowing into their own stack the same day they connect, sitting alongside the integrations that feed the platform from the other direction. The two together mean the platform is open at both ends: it reads from the systems where your truth already lives, and it writes everything it learns back out to wherever you need it, with no lock-in in either direction.
An experiment I ran
I rebuilt a publisher’s entire content QA around an API check for extractability before publish

I wired our citation and extractability signals straight into a [national publisher]’s publishing pipeline through the API, so every draft got scored for machine-readability before it was allowed to go live. The experiment was simple: does catching extractability problems before publish beat chasing them down after.

It is not close. Pages that passed the pre-publish check got cited far sooner than the old publish-then-pray flow, because the structural problems simply never shipped in the first place. Putting the signal at the point of creation, through the API, changed the outcome more than any heroic after-the-fact audit ever did.

If you only measure quarterly, you see that it changed. You never see why.

HOT TAKE · THE PART NOBODY SAYS OUT LOUD
If your AEO data cannot leave the dashboard, it cannot scale, period

A metric you can only read by logging into a UI is hard-capped at human attention, and human attention does not scale. The brands doing this at national scale do not want to log in and stare at a chart. They want the signal inside their own systems, automated, firing at the exact moment it is useful.

That is the entire point of an API: programmatic access so citation and visibility data can drive real workflows, alerts, and pre-publish checks without a human babysitting it. A closed dashboard is a demo you give once. An open API is infrastructure you build a business on.

WHY THIS BEATS THE PASTE-AND-SHIP SHOPS
Most AEO tools have no API because there is no system underneath

A lot of AEO products ship without a real API, and the reason is quietly damning: there is no clean data model under the hood to expose in the first place. The dashboard is the product, and the dashboard is the whole of it. You cannot offer programmatic access to a pile of screenshots, no matter how nice they look.

A documented API is only ever possible when the underlying measurement is structured and consistent, built by engineers who treated the data as a system from the very first day. That is the whole difference between a tool and infrastructure. We can expose the data because we built it from the start to be exposed.

No API is usually a confession: there is no real data model to share.

Put your AI visibility data wherever you need it.
Book a demo and we will walk through the API and how to wire it into your existing reporting.
Book a Platform Demo
Frequently asked questions
What does the API return?
Visibility scores, citation share, AI-referred traffic, generative impressions, and crawler activity, mirroring the dashboard.
Can I send it to my warehouse?
Yes. Teams pipe the metrics into BI and warehouses and blend them with their own revenue data.
How is access secured?
Token-based access, with scheduled pulls and event webhooks for alerting.
References
  1. 1. Google Search Central. Search Console API documentation.
  2. 2. OpenAI. Crawler and access documentation.
  3. 3. Anthropic. API reference documentation.
  4. 4. Perplexity. Sonar API documentation.